Facility management benchmarking: Measuring performances using multi-attribute decision tools

The aim of this research is to develop and demonstrate the applicability of three different decision tools in facility management (FM) benchmarking. The tools are Analytic Hierarchy Process (AHP), Data Envelopment Analysis (DEA) and regression analysis.; Within this research, FM is defined as a process of service operations.; There is a rich body of literature on performance measurement, FM, benchmarking, and decision tools. However, there is a lack of understanding of how the most useful information and knowledge can be acquired through FM benchmarking with the application of decision tools.; Since the early 1990s, research in FM and benchmarking has stressed the importance of objective measurement based on objective and subjective data. This research presents methods which show how the data could be integrated for improvement execution.; The performance of FM covers both hard aspects and soft aspects. Analysis of soft data was carried out with AHP and regression analysis. The relationships between various hard data and soft data were examined by DEA.; As a process of service operations, FM is interpreted as an input-output system which can be assessed by DEA. As with many service industries, customer satisfaction is an important factor within the input-output system. This explains the need for applying decision tools for FM benchmarking. AHP and regression analysis are identified as appropriate tools to analyze soft data. The theoretical discussions are supported by two case studies.; This research shows how the proposed tools can be applied to improve the optimization of resources of FM units and thus improve their competitiveness.; This study reveals the inconsistency of the customers' perceptions on FM quality. With reference to the conventional performance-gap analysis by comparison, significant improvements are made to the analysis methodology by introducing the concepts of matching between soft and hard data and correlating AHP and regression results.; The major contributions from this research are: (1) Integration of the knowledge of decision tools with that of FM benchmarking. (2) Provision of comprehensive design principles for a FM benchmarking framework. (3) Assistance to facility managers to develop a clear picture of their facility's operation with the proposed decision tools.